Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
469924 | Computer Methods and Programs in Biomedicine | 2006 | 14 Pages |
Abstract
In multilevel modeling, researchers often encounter data with a relatively small number of units at the higher levels. As a result, of this and/or non-normality of the residuals, model parameter estimates, particularly the variance components and standard errors of parameter estimates at the group level, may be biased, thus the corresponding statistical inferences may not be trustworthy. This problem can be addressed by using bootstrap methods to estimate the standard errors of the parameter estimates for significance testing. This study illustrates how to use statistical analysis system (SAS) to conduct nonparametric residual bootstrap multilevel modeling. Specific SAS programs for such modeling are provided.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jichuan Wang, James R. Carpenter, Michael A. Kepler,